Home-based monitoring of lower urinary tract health: simultaneous measures using wearable near infrared spectroscopy and linked wireless scale
Bibliographic record
Abstract
Background: Worldwide <4 billion people suffer from urinary tract symptoms that negatively affect quality of life and incur significant cost. Currently, clinical assessment requires invasive urodynamic testing; as this involves catheterization in clinic/hospital settings, only periodic assessment is possible. This is problematic as treatment requirements vary over time; home-based monitoring/assessment able to optimize care would benefit patients and physicians. Methods: A monitoring system integrating a wireless uroflow scale with a wearable transcutaneous near infrared spectroscopy (NIRS) device was designed incorporating end-user feedback, and the feasibility of home use to asses voiding function tested. The NIRS device is worn superior to the pubis during voiding. Light emitting diodes (wavelengths of 760 and 850 nm) allow transcutaneous monitoring of changes in chromophore concentration in the bladder detrusor muscle. The scale collects the urine voided, recording volume increments of 1cc. Data are transmitted wirelessly; incorporated software generates graphs of NIRS chromophore parameters indicative of hemodynamic and oxygenation effects as the bladder contracts, and uroflow data (total volume, mean and average flow rate, peak flow and pattern of uroflow). Results: Serial measures were recorded during spontaneous voiding by an asymptomatic 59-year-old male and a 78-yearold male with lower urinary tract symptoms. During each void, consistent NIRS-derived changes in chromophore concentrations individual to each subject were seen, and simultaneous uroflow measurements successfully recorded. Conclusion: Home based non-invasive NIRS monitoring of bladder function with simultaneous measurement of uroflow is feasible. This technology is capable of providing the repeated measures required to optimize disease monitoring and treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".